app.py

app.py

@@ -1,20 +1,51 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+# Input data
+x1 = [50, 60, 70, 80, 90]
+x2 = [20, 21, 22, 23, 24]
+y_actual = [30, 35, 40, 45, 50]
 
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("distilbert-base-uncased")
-model = AutoModelForSequenceClassification.from_pretrained("distilbert-base-uncased", num_labels=1)
+# Learning rate and maximum number of iterations
+alpha = 0.01
+max_iters = 1000
 
-# Input text
-text = "I want to book a flight from New York to London on July 1st."
+# Initial values for Theta0 and Theta1
+Theta0 = 0
+Theta1 = 0
+Theta2 = 0
 
-# Tokenize input text
-inputs = tokenizer(text, padding=True, truncation=True, return_tensors="pt")
+# Start the iteration counter
+iter_count = 0
 
-# Predict flight ticket
-with torch.no_grad():
-    logits = model(**inputs).logits
-    prediction = torch.sigmoid(logits).item()
+# Loop until convergence or maximum number of iterations
+while iter_count < max_iters:
+    # Compute the predicted output
+    y_pred = [Theta0 + Theta1 * x1[i] + Theta2 * x2[i] for i in range(len(x1))]
 
-# Display prediction
-print("Flight ticket prediction:", prediction)
+    
+    # Compute the errors
+    errors = [y_pred[i] - y_actual[i] for i in range(len(x1))]
+    
+    # Update Theta0 and Theta1
+    Theta0 -= alpha * sum(errors) / len(x1)
+    Theta1 -= alpha * sum([errors[i] * x1[i] for i in range(len(x1))]) / len(x1)
+    Theta2 -= alpha * sum([errors[i] * x2[i] for i in range(len(x2))]) / len(x2)
+    
+    # Compute the cost function
+    cost = sum([(y_pred[i] - y_actual[i]) ** 2 for i in range(len(x1))]) / (2 * len(x1))
+    
+    # Print the cost function every 100 iterations
+    if iter_count % 100 == 0:
+        print("Iteration {}: Cost = {}, Theta0 = {}, Theta1 = {}".format(iter_count, cost, Theta0, Theta1, Theta2))
+    
+    # Check for convergence (if the cost is decreasing by less than 0.0001)
+    if iter_count > 0 and abs(cost - prev_cost) < 0.0001:
+        print("Converged after {} iterations".format(iter_count))
+        break
+    
+    # Update the iteration counter and previous cost
+    iter_count += 1
+    prev_cost = cost
+
+# Print the final values of Theta0 and Theta1
+print("Final values: Theta0 = {}, Theta1 = {}".format(Theta0, Theta1, Theta2))
+print("Final Cost: Cost= {}".format(cost))
+print("Final values: y_pred = {}, y_actual = {}".format(y_pred, y_actual))